The present invention relates to automated localization and identification of vertebrae in medical images, and more particularly, to automated deep-learning based localization and identification of vertebrae in 3D computed tomography (CT) volumes.
Accurate and automatic localization and identification of human vertebrae in 3D spinal imaging is important for clinical tasks such as pathological diagnosis, surgical planning, and post-operative assessment of pathologies. Specific applications, such as vertebrae segmentation, fracture detection, tumor detection and localization, registration, and statistical shape analysis can benefit from efficient and precise automated vertebrae detection and labeling algorithms. However, such automated vertebrae detection and labeling algorithms must address various challenges including pathological cases, image artifacts, and limited field-of-view (FOV). Various approaches for automated vertebrae detection have been developed to address these challenges. However, a method for automatic vertebrae localization and identification that can provide improvements in accuracy and efficiency over existing approaches is desirable.